Tomer - Monitoring Choices Affect our Discernment of Watershed Processes

Monitoring Choices

Affect Our Discernment

of Watershed Processes

and Weather Controls on

Conservation Effectiveness

Mark Tomer

USDA/ARS

National Laboratory for Agriculture and the Environment

Driving questions

How can monitoring designs be chosen to provide the right information?

How do we manage tradeoffs among contaminants (e.g., NO3-N vs. P)? Can monitoring of multiple contaminants help answer this, or must we always choose the lesser of two pollutants?

Along what key pathways are contaminants being transported? How can monitoring efforts help identify transport pathways and sources?

Can monitoring results indicate what types of conservation practices could achieve water quality improvement and where to place them?

What is the role of (so called) extreme events in transport of agricultural pollutants? What monitoring duration do we need to discern this?

Can we use monitoring data to characterize conservation performance beyond a simple ‘% removal’ metric?

Elevation

384m

285m

10 Km

Gauge Grab

station site

Stream

Tile

Field

South Fork Iowa River

Watershed

Two Case Studies

Field Flume TC101 (10.6 ha)

Upper Tipton watershed - Tile

drained, farmed wetlands (potholes)

Phosphorus

concentrations in

two tiles and

Tipton Cr. outlet,

2005-2007

Tipton Creek

0

0.5

1

1.5

2

2.5

3

3.5

4

Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08

Date

To

tal P

, m

g L

-1

0

0.5

1

1.5

2

2.5

3

3.5

4

Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08

Date

To

tal P

, m

g/L

Large tile Small tile

Monitoring Sept. 2006 event

Three scales – field runoff, two tile outfalls, watershed outlet.

Automated sampling

Samples measured for NO3-N, Total P, and E. coli and monitoring of hydrologic discharge at all three scales.

Sediment at the watershed outlet with 7Be:210Pb nuclide analyses to estimate sediment source (channel vs. sheet & rill)

Context of event

Dry antecedent conditions

Late summer, full cover of mature crops

Large event, but small hydrologic

response

Peak discharge was about one half of

bank full discharge

"

"

Rainfall (mm)

47.9 - 57.6

57.7 - 67.2

67.3 - 76.9

77.0 - 86.6

" Rain gauge

Tile

Field

Rainfall event, Sept. 10-11 2006

0

20

40

60

80

100

120

140

9/10/06

0:00

9/10/06

12:00

9/11/06

0:00

9/11/06

12:00

9/12/06

0:00

Date & time

Ra

infa

ll, m

m

Tile Field

Hydrologic response to rainfall

event at three scales

0.0001

0.001

0.01

0.1

1

10

10-Sep 11-Sep 12-Sep 13-Sep 14-Sep 15-Sep 16-Sep 17-Sep

Date (2006)

Q, m

m h

r-1

Stream Field Tile

Note double peak;

First for runoff, then

for tile flow

Field flume: discharge, nutrients,

and E. coli

0.0

2.0

4.0

6.0

8.0

10.0

12.0

10-Sep 11-Sep 12-Sep

Date (2006)

NO

3-N

& to

tal P, m

g L

-1

ln (

E. co

li),

mp

n 1

00

mL

-1

0

10

20

30

40

50

60

Q, L s

-1

NO3-N total P ln E. coli Q

Tile outfalls:

discharge,

nutrients,

and E. coli

0

5

10

15

20

25


Date (2006)

NO

3-N

& to

tal P

, m

g L

-1

ln E

.co

li m

pn

10

0m

L-1

0

100

200

300

400

500

600

700

Q, L

s-1

NO -N total P ln E. coli Q

0

5

10

15

20


Date (2006)

NO

3-N

& to

tal P

, m

g L

-1

ln E

.co

li m

pn

10

0m

L-1

0

5

10

15

20

25

30

35

Q, L

s-1

Nitrate-N Total P ln E. coli Outlet Q

Large tile (TC240)

Small tile (TC242)

Hydrograph

separations at

tile outlets

based on

NO3-N

mixing model

0

100

200

300

400

500

600

700


Date (2006)

Q, L

s-1

Q Q (tile)

0

5

10

15

20

25

30

35


Date (2006)

Q,

L s

-1

Q Q (tile)

Large tile (TC240)

Small tile (TC242)

Stream outlet: discharge,

nutrients, and E. coli

0

5

10

15

20

25


Date (2006)

NO

3-N

& to

tal P, m

g L

-1

ln (E

.co

li),

mp

n 1

00

mL-1

0

1

2

3

4

5

6

Q, m

3s

-1

NO3-N Total P ln (E. coli) Q

Hydrograph separation – Stream outlet

0

1

2

3

4

5

6


Date (2006)

Q, m

3 s

-1

Q Q (tile) Q (ground water)

Cumulative NO3-N loads at tile

and stream gauges

0

100

200

300

400

500

600

700

800

900

1000

9/10 9/11 9/12 9/13 9/14 9/15 9/16 9/17

NO

3-N

lo

ad

, g h

a-1

Stream Tile

0

100

200

300

400

500

600

700

800

900

1000

9/10 9/11 9/12 9/13 9/14 9/15 9/16 9/17

NO

3-N

lo

ad

, g h

a-1

Stream Tile

Sediment response:

78% from channel sources*

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


Date (2006)

Se

dim

en

t, k

g m

-3

Fra

ctio

n s

ed

ime

nt

0

1

2

3

4

5

6

Dis

ch

arg

e, m

3 s

-1

Sediment concentrationg/m3

Fraction sediment fromfield erosion

Stream discharge

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


Date (2006)

Se

dim

en

t, k

g m

-3

Fra

ctio

n s

ed

ime

nt

0

1

2

3

4

5

6

Dis

ch

arg

e, m

3 s

-1

Sediment concentrationg/m3

Fraction sediment fromfield erosion

Stream discharge

*estimated on 7Be/210Pb nuclide ratios

Note: peak sediment concentration

occurred before hydrograph peak

Cumulative total P loads at tile,

field and stream gauges

0

10

20

30

40

50

60

70


Date (2006)

To

tal P

lo

ad

, g

ha-1

Stream Field Tile

Cumulative E. coli loads at tile, field

and stream gauges

0

2000

4000

6000

8000

10000

12000

14000


Date (2006)

E. co

li -

10

6 c

fu h

a-1

Stream Tile Field

Major

sources and

pathways:

Highly erodible crop land

Okoboji/Harps soils

Surface

Drainage districts

Sub-surface

• Subsurface (tile)

• Surface

• Channel

Study One - Conclusions NO3-N was dominantly sourced from tiles (>90%).

Sediment was dominantly (78%) sourced from stream banks.

Surface intakes draining depressions found an important source of P, along with stream sediments.

E. coli was dominated by near- and in-channel sources, although runoff and tile intake sources also contributed.

Conservation emphases on erosion control and nutrient management in this watershed should be expanded to include vegetative practices that stabilize/restore streams and buffer surface intakes that drain potholes.

This single event analysis helped clarify source pathways of key contaminants, helping to inform a more comprehensive approach to water quality management.

Driving questions

How can monitoring designs be chosen to provide the right information?

How do we manage tradeoffs among contaminants (e.g., NO3-N vs. P)? Can monitoring of multiple contaminants help answer this, or must we always choose the lesser of two evils?

Along what key pathways are contaminants being transported? Can monitoring efforts help identify transport pathways and sources?

Can monitoring results indicate what types of conservation practices could achieve water quality improvement and where to place them?

What is the role of (so called) extreme events in transport of agricultural pollutants? What monitoring duration do we need to discern this?

Can we use monitoring data to characterize conservation performance beyond a simple ‘% removal’ metric?

Transition to study two

Study one comprised detailed and nested

monitoring (at 3 scales) of multiple

contaminants during a single rainfall runoff

event (seven days)

Study two compared two fields for total P

transport and runoff amounts during

eleven years.

Elevation

384m

285m

10 Km

Gauge Grab

station site

Stream

Tile

Field

South Fork Iowa River

Watershed

SF101

SF102

Second Case Study

A tale of two fields: HOW DO RUNOFF AND NUTRIENT LOADS DIFFER BETWEEN THEM?

one manured: SF101 one not: SF102

SF101

(manured)

SF102

(not manured)

Rainfall /

runoff

record

(daily)

0

20

40

60

80

100

120

140

160

180

2000

5

10

15

20

25

30

35

40

45

50

Jan-0

0

Ju

l-00

Jan-0

1

Jul-01

Jan-0

2

Jul-02

Jan-0

3

Ju

l-03

Jan-0

4

Jul-04

Jan-0

5

Ju

l-05

Jan-0

6

Ju

l-06

Jan-0

7

Jul-07

Jan-0

8

Ju

l-08

Jan-0

9

Jul-09

Jan-1

0

Ju

l-10

Runoff

-pro

ducin

g p

recip

itation,

mm

Surf

ace

runoff

, m

m

Date

Runoff Rainfall

0

20

40

60

80

100

120

140

160

180

2000

5

10

15

20

25

30

35

40

45

50

Jan-0

0

Jul-00

Jan-0

1

Jul-01

Jan-0

2

Jul-02

Jan-0

3

Jul-03

Jan-0

4

Jul-04

Jan-0

5

Jul-05

Jan-0

6

Jul-06

Jan-0

7

Jul-07

Jan-0

8

Jul-08

Jan-0

9

Jul-09

Jan-1

0

Jul-10

Runoff

-pro

ducin

g p

recip

itation,

mm

Surf

ace runoff

, m

m

Date

Runoff Rainfall

SF101

SF102

Similarity in amounts of

rainfall and runoff per event

y = 1.01xR² = 0.83

0

20

40

60

80

100

120

0 20 40 60 80 100 120

SF10

2 ra

in (m

m)

SF101 rain (mm)

y = 1.16xR² = 0.77

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30SF

102

run

off

(mm

)SF101 runoff (mm)

Significant difference in runoff – P load relationship

y = 0.018x0.947

R² = 0.870

y = 0.018x1.118

R² = 0.858

0.001

0.01

0.1

1

10

0 10 20 30 40

P lo

ss d

uri

ng

eve

nt (k

g/h

a)

Amount of runoff (mm/event)

Not Manured Manured

In-Field Conservation Practices Impact on

Runoff-P Load Relationship Could Improve

Effectiveness of Edge of Field Practices

0.001

0.01

0.1

1

10

0.01 0.1 1 10 100

P lo

ss d

uring

eve

nt (k

g/h

a)

Amount of runoff (mm/event)

Reduce runoff amounts

PL = aQb

Study two: Conclusions

Eleven years of monitoring provided data for >90 rainfall

runoff events in two field-sized watersheds differing in

manure application.

Long periods with little or no runoff were punctuated with

flashy runoff events.

Half the cumulative runoff observed in 11 yrs occurred in

<48 hours.

The two watersheds were similar in rainfall and runoff

amounts.

Study Two Conclusions: P losses

P losses characterized:

P losses averaged about 1.80 kg/ha.yr in the manured watershed

and 1.05 kg/ha.yr in the non-manured watershed.

Differences in the relationship between runoff and P losses were

observed – implications for assessment of practices, and on the

performance of additional practices placed below the field edge.

Large events placed in context:

Storms <60 mm resulted in 84-88% of the observed P load; more

than half the P load was associated with 30-60 mm rainfall events

in both watersheds.

Conservation practices that limit runoff from <60 mm storms should

also limit P losses from these soils.

Driving questions Can monitoring designs be chosen to provide the right information?

Yes, consider goals and options for TIMING, FREQUENCY, NESTING and DURATION of monitoring.

How do we manage tradeoffs among contaminants (e.g., NO3-N vs. P)? Can monitoring of multiple contaminants help answer this, or must we always choose the lesser of two pollutants?

Along what key pathways are contaminants being transported? Can monitoring efforts help identify transport pathways and sources?Contaminants may have unique sources and pathways, which nested, detailed monitoring can help to characterize.

Can monitoring results indicate what types of conservation practices could achieve water quality improvement and where to place them?YES, information on contaminant sources and pathways can help identify appropriate practices to address multiple contaminants, at least in a general way.

What is the role of (so called) extreme events in transport of agricultural pollutants? What monitoring duration do we need to discern this?Decade or more of monitoring needed to place large events in context.

Can we use monitoring data to characterize conservation performance beyond a simple ‘% removal’ metric?Suggestion to characterize runoff – nutrient load relationship when evaluating practice effectiveness.

ThanksCo-Authors

Kevin Cole

Tom Moorman

Tom Isenhart

John Kovar

Dave Heer

Chris Wilson

Technical supportKelly Barnett

Beth Douglass

Amy Morrow

Jeff Nichols

PartnersSouthfork Watershed Alliance

USDA-NRCS